Parametric Study of Dual-particle Swarm Optimisation-modified Adaptive Bats Sonar Algorithm on Multi-objective Benchmark Test Functions

Abstract

An integrated algorithm for solving multi-objective optimisation problems using a dual- level searching approach is presented. The proposed algorithm named as dual-particle swarm optimisation-modified adaptive bats sonar algorithm (D-PSO-MABSA) where the concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is combined with the established particle swarm optimisation algorithm. The proposed algorithm combines the advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handling the complexity of multi-objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi- objective optimisation benchmark test functions. The acquired results show that the proposed algorithm performs well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving multi-objective optimisation problems.